Python finds its applications far and wide across the digital world. Be it web development, data science, data analytics, AI, and whatnot. Even in the case of Computer Vision, Python is the only language that comes to mind. Especially when it comes to the subdomain of facial recognition, industrial applications are far-reaching. This technology is being used to validate human faces through images and videos. And to support it, there are numerous libraries in Python that power face detection, recognition, tracking, and authentication.
Let’s take a look at some of these well-established libraries. face_recognition has an active ecosystem with more than 40k GitHub stars and 11k forks. While the last major release was a year ago, it remains the simplest face recognition library with a model which has 99.38% accuracy. We also have the faceswap library, which is developed by Deepfakes. Powered by Keras, Tensorflow, and Python, it is a leading free and Open Source multi-platform software that runs on all major operating systems. FaceNet is another face recognition system that’s been developed by researchers at Google. It takes the person’s face as input and outputs a vector of 128 numbers representing the most important features of a face. It leverages triplet loss along with deep convolutional networks to achieve the highest order of accuracy.
Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.